Changing trends in safer opioid supply uptake, dose and hydromorphone volume in Ontario: A population-based cross-sectional study
Bibliographic record
Abstract
INTRODUCTION: Safer opioid supply involves prescribed pharmaceutical opioids as alternatives to unregulated drug supply for people at high-risk of opioid toxicity. In Canada, this is primarily achieved through prescribing immediate-release hydromorphone, often combined with methadone. This study evaluated safer opioid supply prescribing in Ontario, Canada. METHODS: We conducted a population-based repeated cross-sectional study to analyze monthly trends in immediate-release hydromorphone prescribing for safer opioid supply, dosing patterns, and proportion of oral hydromorphone dispensed for safer opioid supply in Ontario from 2016 to 2023. We used Joinpoint regression to identify changes in uptake and dosing, and summarized safer opioid supply recipient characteristics in 2023. RESULTS: Monthly safer opioid supply participation was low before January 2019 (range 0.18 -0.47 per 100,000), before slowly rising (+0.09 per 100,000 monthly), until June 2020 when it rapidly increased (+0.54 per 100,000, monthly). In 2023, among 2730 safer opioid supply recipients, 62.3 % were male (mean age, 42 years). During the study period, mean immediate-release hydromorphone doses for safer opioid supply also rose, with notable accelerations occurring between October 2018 and December 2019, and again between April 2020 and January 2021. By December 2023, mean daily immediate-release hydromorphone doses reached 172 mg, and safer opioid supply programs accounted for 22.5 % of all oral hydromorphone dispensed in Ontario. CONCLUSIONS: An accelerating number of safer opioid supply recipients over time, coupled with rising immediate-release hydromorphone doses likely reflects efforts to meet the needs of people who use drugs in the context of an increasingly potent unregulated opioid supply, particularly as this supply changed throughout the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".